The methods mentioned above mainly address the general spatio-temporal related problems, which involve significant inter-class variations. However, intra-class variations could be dominant in many applications such as FoG detection.
Hence, fine-grained architectures are proposed for the applications with subtle inter-class variations. In summary, there are two major approaches of these architectures.
Firstly, the patch-based methods (e.g. [1]}, [2]} and our recent one [3]}) have been proposed to model the subtle variations by utilizing regional or patch based patterns to characterize inter-class variations.
The patch-based approaches are able to further analyze the localized key patterns.
However, for footstep pressure sequences, it is not straightforward to identify such patches, considering the data modality as shown in Fig.REF .
Secondly, a bilinear pooling approach is proposed to eliminate the need for region related prior knowledge [4]}.
Nonetheless, the bilinear pooling increases the model complexity, whilst footstep pressure sequence data is sensitive to complex architectures and the FoG detection performance could be compromised by directly following this approach.
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